96% of organizations run into problems with AI and machine learning projects

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Companies face issues with training data quality and labeling when launching AI and machine learning initiatives, according to a Dimensional Research report. The worldwide spending on artificial intelligence (AI) systems is predicted to hit $35.8 billion in 2019, according to IDC. This increased spending is no surprise: With digital transformation initiatives critical for business survival, companies are making large investments in advanced technologies. However, nearly eight out of 10 organizations engaged in AI and machine learning said that projects have stalled, according to a Dimensional Research report. The majority (96%) of these organizations said they have run into problems with data quality, data labeling necessary to train AI, and building model confidence.


Global Big Data Conference

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Companies face issues with training data quality and labeling when launching AI and machine learning initiatives, according to a Dimensional Research report. The worldwide spending on artificial intelligence (AI) systems is predicted to hit $35.8 billion in 2019, according to IDC. This increased spending is no surprise: With digital transformation initiatives critical for business survival, companies are making large investments in advanced technologies. However, nearly eight out of 10 organizations engaged in AI and machine learning said that projects have stalled, according to a Dimensional Research report. The majority (96%) of these organizations said they have run into problems with data quality, data labeling necessary to train AI, and building model confidence.


96% of organizations run into problems with AI and machine learning projects

#artificialintelligence

The worldwide spending on artificial intelligence (AI) systems is predicted to hit $35.8 billion in 2019, according to IDC. This increased spending is no surprise: With digital transformation initiatives critical for business survival, companies are making large investments in advanced technologies. However, nearly eight out of 10 organizations engaged in AI and machine learning said that projects have stalled, according to a Dimensional Research report. The majority (96%) of these organizations said they have run into problems with data quality, data labeling necessary to train AI, and building model confidence. SEE: Artificial intelligence: A business leader's guide (free PDF) (TechRepublic) The report, conducted by Dimensional Research on behalf of Alegion, surveyed 227 tech professionals who were involved in active AI and machine learning projects.


Data Scientists Worry About Human Bias in Machine Learning, AI-Based Warfare -- ADTmag

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Data scientists are a happy bunch overall, but they do worry about ethical issues such as human bias and prejudice being programmed into machine learning (ML) and the use of artificial intelligence (AI) and automation in warfare and intelligence gathering. That's a finding in the new "2017 Data Scientist Report" just published by AI specialist CrowdFlower Inc. "Read any article on AI (and there is no shortage) and shortly behind, you'll likely find mention of ethical issues," the report said. "From the White House to the Wall Street Journal to the World Economic Forum, the question of how we program the future is one of the most critical issues facing not just data scientists but society as a whole. In perhaps the most important question in this year's survey, we asked, 'Which of the following do you personally think might be issues regarding ethics and AI?' " The top concern raised in answering that question was "human bias/prejudice programmed into machine learning" (listed by 63 percent of respondents), followed by "use of AI and automation in warfare/intelligence" (49 percent). "Unease on the displacement of human workforces and the impossibility of programming a commonly agreed upon moral code also ranked high on the radar of ethical issues for data scientists tallying in at 41 percent and 42 percent respectively," CrowdFlower said.


Big Data: Top 4 Macro Trends - Datamation

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The way enterprises approach big data is rapidly changing. Just a few short years ago, big data was a hot buzzword, and most organizations were only experimenting with Hadoop and related technologies. Today, big data, particularly big data analytics, has evolved to become a critical part of most business's strategies, and organizations are facing intense pressure to keep up with rapid advances in the field. The NewVantage Partners Big Data Executive Survey 2018 found that big data projects -- and the benefits derived from those projects -- have become nearly universal. Among respondents, 97.2 percent of executives said their companies are working on big data or artificial intelligence (AI) initiatives, and 98.6 percent said their firms are trying to create a data-driven culture, up from 85.5 percent in 2017.